Knowledge innovation network is the macro-geographical characterization of the cooperative relationship of micro-innovation subjects. The exploration of the structure of Chinese city tourism knowledge innovation network and its formation mechanism under the internationalization perspective is of great significance for tapping the internationalization level of city tourism knowledge, enhancing inter-city tourism knowledge cooperation and cultivating innovation growth poles. We constructed the tourism knowledge innovation network of Chinese cities with the co-authored thesis data of cities in “Web of science”, and analyzed the spatial and topological evolution characteristics of the network in the time periods of 2011—2014, 2015—2018, 2019−2022. The spatial and topological evolution characteristics of the network are analyzed in three periods of time, and then the weighted exponential random graph model (ERGM) is invoked to quantitatively study the influence of endogenous and exogenous factors on the formation and evolution of the network. The results show: 1) The scale of the network is expanding rapidly, and the overall strength and number of links are steadily increasing, with the spatial pattern changing from a simple structure with Hong Kong as the single center of radiation to a complex network with multiple centers of radiation; 2) The “small world” character of the network is becoming more pronounced, and there is a clear tendency for cities to connect on the basis of merit; 3) The centrality of the nodes in the network varies widely, with Hong Kong, Macau, Guangzhou, and Beijing being the central cities for tourism knowledge innovation; 4) In terms of the driving mechanism, multiple factors such as self-organization, meritocratic connectivity, tourism research investment, domestic tourism income, per capita gross regional product, and social proximity combine to contribute to the formation and evolution of the tourism knowledge and innovation network, with the positive influence of city attribute variables being stronger than that of multidimensional proximity.

Around 2011, the growth rate of China’s economy slowed down, and the fundamentals of the Chinese economy underwent substantial changes. Economic development began to enter a new normal. With the increasingly acute drawbacks of the factor driven economic development model, relying on innovation-driven to shape new driving forces and advantages for development, and achieving the transformation of new and old driving forces for economic growth, has become the key for China to break the shackles of factors and achieve high-quality economic development. As an important bearing space for China to shape new development advantages, cities have already become an important position of innovation-driven development strategy. The improvement of urban innovation-driven level provides a powerful source of power for achieving the goal of Chinese path to modernization. This article is based on the theory of innovation value chain, with technological innovation as the core to construct an urban innovation-driven system. The SBM model of unexpected output super efficiency is used to measure the input-output efficiency of the transformation and diffusion stage of scientific and technological achievements in the urban innovation-driven system, indirectly characterizing the level of urban innovation-driven, identify the spatiotemporal evolution characteristics of innovation-driven level in 284 prefecture level and above cities in China from 2003 to 2017 using the Global Moran’s I and hot spot analysis method, and further analyze the spatiotemporal heterogeneity of factors influencing urban innovation-driven level using the spatiotemporal geographically weighted regression model (GTWR model).The results show that: 1) The overall innovation-driven level of Chinese cities showed a slow growth trend from 2003 to 2017, with an average annual growth rate of 1.32%, fluctuating from 0.307 to 0.369, showing a clear two-stage characteristic. The growth momentum of innovation-driven levels in north China, northeast China, and northwest China is insufficient. The insufficient ability to transform and diffuse scientific and technological achievements, as well as the enormous pressure on carbon reduction, have become the main reasons for the slow growth of innovation-driven level in Chinese cities. 2) The spatial distribution pattern of urban innovation-driven levels has evolved from “high in the west and low in the east” to “high in the south and low in the north”. Correspondingly, the spatial distribution pattern of urban innovation-driven cold and hot spots has evolved from “cold in the east and hot in the west” to “hot in the south and cold in the north”. The spatial distribution of urban innovation-driven growth clusters exhibits a clear “core-edge” feature, which is highly correlated with the spatial distribution of urban clusters, and most provincial capitals/municipalities are regional growth poles. 3) The spatiotemporal evolution of China’s urban innovation-driven level from 2003 to 2017 is the result of a combination of factors, mainly driven by urban affluence and government intervention tendency in the early period, and relying on urban affluence and industrial development level in the later period. In addition, the effect, action intensity and fluctuation direction of each factor on level of urban innovation-driven vary in different regions and periods.

Using panel regression models with time and entity fixed effects and cointegration analysis, the article investigates both internal and external driving factors: 1) Green transportation technology innovation in China is primarily propelled by progress in road transport and enabling technologies in transport, which account for the largest shares, at 62.8% and 51.5%, respectively. 2) The key innovators of innovation has shifted from predominantly individual to enterprise, with firms representing the largest proportion at 82.4%. 3) The spatial distribution of green transportation technology innovation demonstrates notable differentiation and growing concentration, with the Pearl River Delta and Yangtze River Delta emerging as key innovation hubs. Shenzhen has surpassed Shanghai in two fundamental domains: road transport and enabling technologies in transport. 4) External factors such as urban comprehensive transportation accessibility and research and development (R&D) investment universally promote urban green transportation technology innovation at the national scale. In the eastern region, R&D investment and urban comprehensive transportation accessibility exert a stronger positive influence; in the central region, urban scale and R&D investment are the principal driving forces; and in the western region, urban scale, urban transportation logistics industrial location entropy, foreign direct investment, and governmental environmental regulations all contribute to promoting innovation. Compared with green technology innovation, green transportation technology innovation differs significantly in terms of innovation thresholds, the role of foreign investment, environmental regulations, and environmental conditions. Additionally, within green transportation’s internal technological system, innovation in enabling technologies in transport significantly spurs innovation across other categories. This study provides references and insights for the formulation of policy regulation measures tailored to the local context of green transportation technology innovation and development at the national and regional levels.

Using social network analysis and valued exponential random graph models, this paper analyzes the spatial-temporal patterns of China’s artificial intelligence industry’s innovation collaboration network from 2010 to 2021 and quantitatively examines its formation and evolutionary factors from 3 perspectives: network structure, multidimensional proximities and urban characteristics. The key findings are as follows: the artificial intelligence innovation collaboration network of Chinese cities exhibits a hierarchical structure, with Beijing serving as the center hub. Additionally, an urban innovation collaboration network gradually forms a “diamond” pattern comprising Beijing-Tianjin-Hebei region, the Yangtze River Delta region, the Pearl River Delta region, and Chengdu-Chongqing urban agglomeration. The network demonstrates typical heterogeneity attributes along with small-world characteristics and a “core-edge” structure. The dependence of artificial intelligence on technology and industry leads to the comprehensive impact of network self-organization, multi-dimensional proximity and urban innovation cooperation environment. The preferential attachment and the intermediary effect significantly promote network development, while the characteristics of technological proximity of innovation cooperation change from complementary innovation to the coexistence of complementary and collaborative innovation. Geographical distance and inter-provincial barriers no longer impede innovation cooperation. The urban innovation environment foster the breadth and depth of innovation collaboration but the disparities in development levels between cities hinder cooperation establishment.

With the promotion of high-quality development in China, high-quality export development has become an important direction for foreign trade. As an important carrier of China’s ‘going global’ strategy, overseas cooperation zones (OCZs) have gradually become an important measure to promote high-quality export development. This article takes the establishment of the OCZs as a quasi-natural experiment and uses a multi-period difference-in- difference model to study the impact and mechanism of the establishment of the OCZs on China’s export product structure. The results show that the establishment of the OCZs significantly reduces the export share of non-technical products and increases the import share of technical products, and improves the ratio of technical products to non-technical products, optimizing China’s product structure; after parallel trends and a series of robustness tests, this conclusion still holds; Mechanism testing shows that OCZs mainly expand the technology spillover effects of outward foreign direct investment, improve infrastructure construction, enhance institutional quality, in order to achieve the optimization of China’s export product structure. Heterogeneity analysis found that: compared with developed countries (areas), the OCZs have a higher degree of optimization in China’s export product structure in developing countries (areas); The OCZs dominated by Chinese private enterprises have a more significant optimization of China’s export product structure to the host countries (areas); The optimization effect of open cooperation zones on China’s export product structure is mainly reflected in developing countries (areas) and cooperation zones dominated by private enterprises. Further analysis shows that the OCZs have significantly increased the export share of China’s low tech and medical technology products, but have no significant impact on the export proportion of high-tech products. This article provides policy recommendations for China to promote the optimization of its export product structure by establishing OCZs, and also provides empirical evidence for China to accelerate its ‘going global’ strategy to achieve high-quality export development.

In the era of information, the digital economy is not only a crucial driving force for social and economic development, but also an important support in resource management and environmental preservation. Currently, an increasing number of scholars are delving into the intricate dynamics between digital economy and sustainable development. However, previous literature has overlooked a thorough examination of the theoretical logic, interactions, and coupling dynamics. Under this background, this paper first leverages the concept of metacoupling to explore the theoretical relationship for coupling the digital economy with sustainable development at various scales. It then constructs an index system for the digital economy that includes three key dimensions: digital infrastructure, digital economic environment, and digital economic market. Subsequently, the study examines the coupling coordination degree between the digital economy and sustainable development across 106 countries, using Geodetector to explore the interactive relationship among these factors. Finally, it investigates the coupling dynamics of the two from the perspective of the human-land relationship. The study reveals that: 1) The overall level of the global digital economy and sustainable development has been steadily improving, yet distinct “digital divides” and differences in sustainable development persist among regions. 2) There is a strong correlation between the digital economy and sustainable development, with the level of coupling coordination progressing from an initial stage of primary coordination to an intermediate stage. 3) The level of coupling coordination of the digital economy and sustainable development shows significant spatial clustering. Despite an increase in the number of countries achieving high-quality and good coordination, the majority still grapple with the issue of lagging digital economic development. 4) Interaction analysis shows that all factors show two-way enhancement. Among them, SDG1 (No Poverty) and digital infrastructure play the most critical roles in the coupling coordination. 5) Internal and external coupling dynamics are intertwined and jointly drive the coupling evolution, including internal data productivity, digital technology transformation and destruction, and natural resource support, as well as external market pulling and imbalance forces, and government regulatory and hindering forces.

An in-depth understanding of the evolutionary characteristics and formation mechanism of the global tourism service trade network has an important enlightenment effect on China’s ability to enhance the competitiveness of service trade. Based on the data of bilateral tourism service trade of 201 countries from 2001 to 2019 in the WTO-OECD Balanced Trade in Services Database (BaTIS), this thesis Uses Complex Network Analysis and Temporal ExponenTial Random Graph Model (TERGM) to study the dynamic evolution characteristics of topological structure, spatial structure and the community structure, and the evolution mechanism of the network. The results found that: 1) The global tourism service trade relations between countries are becoming increasingly deepened, and the trade network has small world attributes. China has played a prominent bridge role, its position in the global tourism service trade network has significantly improved, indicating that China’s control over global tourism resources and markets is gradually strengthening. 2) The global tourism service trade network exhibits different group characteristics at different time. There are mainly 3 stable communities in the global tourism service trade network, which are dominated by the United States, the United Kingdom and China. 3) Tourism service trade relations have obvious regional differences, showing the network pattern is “one superpower and few major powers”, the Asia Pacific region is developing rapidly. 4) The results of the temporal exponential random graph model show that there are reciprocity effect, hierarchical effects and time dependence effect in tourism service trade network. The level of economic development, population size, the border network, language network and free trade agreement network have positive promoting effects on tourism service trade relations. Additionally, compared to countries with high trade volumes, countries with lower trade volumes tend to engage in tourism service trade.

Based on the key research perspective of ‘the difference of status and role of the same city in different scale spaces’, which has been generally ignored in existing urban network studies, this paper takes the population flow between Chinese cities as the analysis path based on Tencent migration big data, and establishes a multi-scale network analysis model covering three levels: metropolitan area, urban agglomeration and national. By analyzing and comparing the multi-scale pattern and cross-scale changes of this intercity population flow network features, the special cities are explored from the multi-scale perspective, and the reasons for the emergence of special cities are initially explained. On the basis of describing the overall pattern of intercity population flow network at multi-scale, the study not only compared the static pattern of the geometric, quantitative and directional characteristics of the intercity population flow network, but also focused on the change trend of the above three characteristics s in the process of the scale expansion of the metropolitan area-urban agglomeration and urban agglomeration-national network. 1) The ‘core-edge’ characteristics of intercity population flow network in China are obvious at the ‘national level’ and ‘metropolitan area level’, while in the ‘urban agglomeration level’, there is a relatively balanced urban community. 2) The regional economic center cities mostly absorb people from the national level network and transport it to the lower level network. 3) In intercity population flow network at multi-scale, the cities with the strongest Closeness Centrality in the same city community are generally stable, while the cities with the strongest Weighted Degree Centrality change more, which reflects that the ‘population mobility scheduling’ ability of each city in the intercity population flow network is more susceptible to the impact of the spatial scale. 4) The population flow between most cities is more active in the metropolitan circle level network and the national level network, but some cities link a wider population base, more convenient circulation path, and are subject to stronger push and pull forces in the urban agglomeration-level network, which explains why the intercity flow activity of these cities also peaks in the urban agglomeration-level network. In the perspective of multi-scales, the differences in status and role of the same city in different scales of space are very obvious. Taking a multi-scales research perspective in urban network studies can help understand the full picture of each city’s role, which is of great value for understanding the network characteristics of cities and formulate the relevant planning and management policies for the coordinated development of urban areas.

The inheritance and development of Chinese vernacular housing and the impact of population migration on these are important topics in the research of vernacular housing. However, due to the spatial and temporal limitations of traditional case surveys, studies conducted from architectural perspectives often lack the ability for inter-field comparisons. As a result, the similarities in the overall spatial patterns of Chinese vernacular housing remain unclear. Satellite remote sensing holds significant potential for addressing these challenges because of its extensive coverage and consistent spatial-temporal criteria. However, in vernacular housing research, remote-sensing interpretations rely heavily on manual processing and drawing, limiting the depth and efficiency of interpretation necessary for effectively measuring similarities. Measuring similarities in vernacular housing through intelligent remote-sensing interpretation presents considerable challenges. This paper proposes a method for measuring vernacular housing similarities by utilizing intelligent remote-sensing interpretation in conjunction with large amounts of satellite imagery. This method combines expert knowledge with deep learning models to achieve effective similarity measurement and mapping, resulting in an accuracy of 79.1% for classifying classical vernacular housing and a representative ability of 77.1% across all imagery features. Using this method, we assessed the vernacular housing similarities from the Hakka perspective throughout China. The effectiveness of feature extraction and similarity measurement was validated by analyzing Hakka migration routes, ancient postal roads, and village documents. The findings indicate that population migration in southern China during the Ming and Qing dynasties significantly influenced the spatial patterns of Hakka vernacular housing similarities. In conclusion, this study offers a new perspective on vernacular housing and migration research.

The study is based on the maximum value composite MODIS NDVI data of growing seasons (GNDVI) from 2000 to 2020 in the Three River Headwater Region (TRHR). It uses trend analysis and spatial analysis methods to quantify changes in grassland greenness in the region. Additionally, correlation and partial correlation analyses are applied to explore the relationship between temperature, precipitation, and GNDVI at different temporal and spatial scales. The results of the study show that: 1) From 2000 to 2020, the overall trend of grassland GNDVI in the TRHR has increased, with 77.53% of pixels showing an increasing trend. Among these, 33.95% of pixels show a significant increase (P<0.1). On the other hand, a decrease is observed in some areas, with 22.47% of pixels showing a decreasing trend, and 3.03% of these showing a significant decrease (P<0.1). 2) The pixels with a significant increase in GNDVI are mainly found at elevations of 4500-5000 m and on north-facing slopes with a gradient of 2°-6°. Conversely, the pixels with a significant decrease in GNDVI are primarily located at elevations of 4500-5000 m and on south-facing slopes with a gradient of 6°-15°. 3) Overall, in the TRHR, GNDVI shows the strongest correlation with temperature and precipitation during the growing season. The correlation with the minimum temperature during the growing season (R=0.79, P<0.001) is stronger than with precipitation (R=0.66, P<0.001) and average temperature (R=0.55, P<0.001). The relationship between monthly climate factors at the grid scale and GNDVI shows that the interannual fluctuations of GNDVI are most strongly correlated with precipitation and minimum temperature in July. Spatially, the eastern GNDVI is primarily influenced by precipitation, while the western GNDVI is mainly driven by temperature.

Based on daily precipitation data from 1970 to 2020, we analyzed the spatio-temporal variation of precipitation seasonality index (PSI) in south and north Qinling Mountains. Then, the empirical orthogonal function (EOF) analysis is performed to identify the leading spatial patterns of PSI in the study region. More specially, we discussed the relationship between the leading spatial patterns of PSI and sea surface temperature anomaly (SSTA). The results show that: 1) The change of PSI in south and north of the Qinling Mountains was mainly synchronous variation over the past 51 years. Before 1997, it could be observed one peak (dry) periods (1975—1986) and two valley (wet) periods (1970—1975 and 1987—1996) of PSI variation. After 1997, the precipitation showed markedly seasonality with a long drier season in 1997—2015, which indicated the dry climate is becoming the normal condition for China’s south-north transitional geographical zone. 2) Spatially, the single type of precipitation seasonality is clearly seen over most regions (61.3% of the study area) and the combined type of precipitation seasonality (32.7% of the study area) does not prevail. In detail, for the single type, the eastern part of Hanjiang River Basin and western part of Daba Mountains (28.3% of the study area) are mainly controlled by a longer wet season. Moreover, precipitation seasonality with the dry−wet balance accounted for 22.9% of the study area, which located in the west of Jialing River Basin, Hanzhong Basin, Ankang Basin and the middle of Guanzhong Plain. 3) This study investigates the first leading spatial patterns of the interannual variability of PSI in the south and north Qinling Mountains. The positive phase of the first leading mode (EOF1) showed characterized by positive PSI anomalies for the whole region. The positive phase of EOF1 was significantly associated with the negative phase of North Atlantic Oscillation (NAO) from pre-winter to spring, as well as the transition from El Niño in pre-winter to La Nina in summer.